
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


__lowerCAmelCase     :	Tuple       			=  os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(F'''{bindir}/../../examples/pytorch/translation'''):
    from run_translation import main  # noqa


set_seed(42)
__lowerCAmelCase     :	Union[str, Any]       			=  'sshleifer/student_marian_en_ro_6_1'
__lowerCAmelCase     :	Optional[int]       			=  'sshleifer/tiny-mbart'




@require_torch
class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  def 				_lowercase				(					self     :					Dict     ,				UpperCamelCase__     :					Optional[Any]=False     ,				UpperCamelCase__     :					List[str]=None     ,				UpperCamelCase__     :					Optional[Any]=True     ,				UpperCamelCase__     :					List[str]=True     ,				UpperCamelCase__     :					Optional[Any]=True     ,				UpperCamelCase__     :					List[Any]=True     ,				)     ->      Optional[int]:







    """simple docstring"""






    __magic_name__								=			self.run_trainer(
        eval_steps=1     ,				max_len=12     ,				model_name=UpperCamelCase__     ,				num_train_epochs=1     ,				distributed=UpperCamelCase__     ,				extra_args_str=UpperCamelCase__     ,				predict_with_generate=UpperCamelCase__     ,				do_train=UpperCamelCase__     ,				do_eval=UpperCamelCase__     ,				do_predict=UpperCamelCase__     ,				)
    __magic_name__								=			TrainerState.load_from_json(os.path.join(UpperCamelCase__     ,				"""trainer_state.json"""       )       ).log_history

    if not do_eval:
      return

    __magic_name__								=			[log for log in logs if """eval_loss""" in log.keys()]

    __magic_name__								=			eval_metrics[0]
    if predict_with_generate:
      assert "eval_bleu" in first_step_stats

      __magic_name__								=			eval_metrics[-1]
      assert isinstance(last_step_stats["""eval_bleu"""]     ,				UpperCamelCase__       )
      assert not math.isnan(float(last_step_stats["""eval_loss"""]       )       ), "eval_loss must not be `nan`"
  @require_torch_non_multi_gpu
  def 				_lowercase				(					self     :					List[str]       )     ->      Union[str, Any]:







    """simple docstring"""






    self.run_seqaseq_quick()
  @require_torch_multi_gpu
  def 				_lowercase				(					self     :					List[str]       )     ->      Optional[Any]:







    """simple docstring"""






    self.run_seqaseq_quick(distributed=UpperCamelCase__       )
  @require_torch_multi_gpu
  def 				_lowercase				(					self     :					Any       )     ->      Optional[Any]:







    """simple docstring"""






    self.run_seqaseq_quick(distributed=UpperCamelCase__       )
  @unittest.skip("""Requires an update of the env running those tests"""       )
  @require_torch_multi_gpu
  @require_fairscale
  def 				_lowercase				(					self     :					Union[str, Any]       )     ->      Optional[int]:







    """simple docstring"""






    self.run_seqaseq_quick(distributed=UpperCamelCase__     ,				extra_args_str="""--sharded_ddp simple"""       )
  @unittest.skip("""Requires an update of the env running those tests"""       )
  @require_torch_multi_gpu
  @require_fairscale
  def 				_lowercase				(					self     :					int       )     ->      Tuple:







    """simple docstring"""






    self.run_seqaseq_quick(distributed=UpperCamelCase__     ,				extra_args_str="""--sharded_ddp simple --fp16"""       )
  @unittest.skip("""Requires an update of the env running those tests"""       )
  @require_torch_multi_gpu
  @require_fairscale
  def 				_lowercase				(					self     :					str       )     ->      Any:







    """simple docstring"""






    self.run_seqaseq_quick(distributed=UpperCamelCase__     ,				extra_args_str="""--sharded_ddp zero_dp_2"""     ,				predict_with_generate=UpperCamelCase__       )
  @unittest.skip("""Requires an update of the env running those tests"""       )
  @require_torch_multi_gpu
  @require_fairscale
  def 				_lowercase				(					self     :					str       )     ->      List[Any]:







    """simple docstring"""






    self.run_seqaseq_quick(
        distributed=UpperCamelCase__     ,				extra_args_str="""--sharded_ddp zero_dp_2 --fp16"""     ,				predict_with_generate=UpperCamelCase__       )
  @require_apex
  @require_torch_gpu
  def 				_lowercase				(					self     :					str       )     ->      Optional[int]:







    """simple docstring"""






    self.run_seqaseq_quick(distributed=UpperCamelCase__     ,				extra_args_str="""--fp16 --fp16_backend=apex"""       )
    # test 2nd time - was getting eval_loss': nan'
    # to reproduce the problem set distributed=False
    self.run_seqaseq_quick(distributed=UpperCamelCase__     ,				extra_args_str="""--fp16 --fp16_backend=apex"""       )
  @parameterized.expand(["""base""", """low""", """high""", """mixed"""]       )
  @require_torch_multi_gpu
  def 				_lowercase				(					self     :					str     ,				UpperCamelCase__     :					Dict       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			{
        # test with the default log_level - should be info and thus log info once
        """base""": {"""extra_args_str""": """""", """n_matches""": 1},
        # test with low log_level and log_level_replica - should be noisy on all processes
        # now the info string should appear twice on 2 processes
        """low""": {"""extra_args_str""": """--log_level debug --log_level_replica debug""", """n_matches""": 2},
        # test with high log_level and low log_level_replica
        # now the info string should appear once only on the replica
        """high""": {"""extra_args_str""": """--log_level error --log_level_replica debug""", """n_matches""": 1},
        # test with high log_level and log_level_replica - should be quiet on all processes
        """mixed""": {"""extra_args_str""": """--log_level error --log_level_replica error""", """n_matches""": 0},
    }

    __magic_name__								=			experiments[experiment_id]
    __magic_name__								=			{"""distributed""": True, """predict_with_generate""": False, """do_eval""": False, """do_predict""": False}
    __magic_name__								=			"""Running training"""
    with CaptureStderr() as cl:
      self.run_seqaseq_quick(**UpperCamelCase__     ,				extra_args_str=data["""extra_args_str"""]       )
    __magic_name__								=			len(re.findall(UpperCamelCase__     ,				cl.err       )       )
    self.assertEqual(UpperCamelCase__     ,				data["""n_matches"""]       )
  @slow
  def 				_lowercase				(					self     :					Any       )     ->      List[str]:







    """simple docstring"""






    __magic_name__								=			self.run_trainer(
        eval_steps=2     ,				max_len=128     ,				model_name=UpperCamelCase__     ,				learning_rate=3E-4     ,				num_train_epochs=10     ,				distributed=UpperCamelCase__     ,				)

    # Check metrics
    __magic_name__								=			TrainerState.load_from_json(os.path.join(UpperCamelCase__     ,				"""trainer_state.json"""       )       ).log_history
    __magic_name__								=			[log for log in logs if """eval_loss""" in log.keys()]
    __magic_name__								=			eval_metrics[0]
    __magic_name__								=			eval_metrics[-1]

    assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
    assert isinstance(last_step_stats["""eval_bleu"""]     ,				UpperCamelCase__       )

    # test if do_predict saves generations and metrics
    __magic_name__								=			os.listdir(UpperCamelCase__       )
    __magic_name__								=			{os.path.basename(UpperCamelCase__       ) for p in contents}
    assert "generated_predictions.txt" in contents
    assert "predict_results.json" in contents
  @slow
  @require_bitsandbytes
  def 				_lowercase				(					self     :					List[Any]       )     ->      List[str]:







    """simple docstring"""






    from transformers.training_args import OptimizerNames

    def train_and_return_metrics(UpperCamelCase__     :					str       ) -> Tuple[int, float]:
      __magic_name__								=			"""--skip_memory_metrics 0"""

      __magic_name__								=			self.run_trainer(
          max_len=128     ,				model_name=UpperCamelCase__     ,				learning_rate=3E-4     ,				num_train_epochs=1     ,				optim=UpperCamelCase__     ,				distributed=UpperCamelCase__     ,				extra_args_str=UpperCamelCase__     ,				do_eval=UpperCamelCase__     ,				do_predict=UpperCamelCase__     ,				n_gpus_to_use=1     ,				)

      # Check metrics
      __magic_name__								=			TrainerState.load_from_json(Path(UpperCamelCase__     ,				"""trainer_state.json"""       )       ).log_history
      __magic_name__								=			int(logs[0]["""train_mem_gpu_peaked_delta"""] / 2**20       )
      __magic_name__								=			int(logs[0]["""train_mem_gpu_alloc_delta"""] / 2**20       )

      __magic_name__								=			logs[0]["""train_loss"""]
      return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

    __magic_name__	,       __magic_name__	,       __magic_name__								=			train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value       )
    __magic_name__	,       __magic_name__	,       __magic_name__								=			train_and_return_metrics(OptimizerNames.ADAMW_BNB.value       )

    __magic_name__								=			gpu_alloc_mem_orig - gpu_alloc_mem_bnb

    __magic_name__								=			gpu_peak_mem_orig + gpu_alloc_mem_orig
    __magic_name__								=			gpu_peak_mem_bnb + gpu_alloc_mem_bnb
    __magic_name__								=			gpu_total_mem_orig - gpu_total_mem_bnb

    # sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
    # doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
    # in 2 bytes and the diff in optim memory usage is derived as so:
    #
    # - normal 25*8=~200MB (8 bytes per param)
    # - bnb    25*2= ~50MB (2 bytes per param)
    #
    # Thus we should expect ~150MB total memory saved.
    #
    # Peak memory should be the same - the total should be different by about that same margin
    #
    # After leaving a small margin to accommodate for differences between gpus let's check
    # that we have at least 120MB in savings
    __magic_name__								=			120

    # uncomment the following if this test starts failing - requires py38 for a new print feature
    # gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
    # print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
    # print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
    # print(f"{gpu_alloc_mem_diff=}MB")
    # print(f"{gpu_peak_mem_diff=}MB")
    # print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
    # print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

    self.assertGreater(
        UpperCamelCase__     ,				UpperCamelCase__     ,				"""should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got"""
        F''' a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and'''
        F''' gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB'''     ,				)

    self.assertGreater(
        UpperCamelCase__     ,				UpperCamelCase__     ,				"""should use ~150MB less total gpu memory with BNB, compared to without it for this model but got"""
        F''' a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and'''
        F''' gpu_total_mem_bnb={gpu_total_mem_bnb}MB'''     ,				)

    self.assertEqual(
        UpperCamelCase__     ,				UpperCamelCase__     ,				F'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'''       )





  def 				_lowercase				(					self     :					Tuple     ,				UpperCamelCase__     :					int     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					int     ,				UpperCamelCase__     :					float = 3E-3     ,				UpperCamelCase__     :					str = "adafactor"     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					str = None     ,				UpperCamelCase__     :					int = 0     ,				UpperCamelCase__     :					bool = True     ,				UpperCamelCase__     :					bool = True     ,				UpperCamelCase__     :					bool = True     ,				UpperCamelCase__     :					bool = True     ,				UpperCamelCase__     :					int = None     ,				)     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
    __magic_name__								=			self.get_auto_remove_tmp_dir()
    __magic_name__								=			F'''
            --model_name_or_path {model_name}
            --train_file {data_dir}/train.json
            --validation_file {data_dir}/val.json
            --test_file {data_dir}/test.json
            --output_dir {output_dir}
            --overwrite_output_dir
            --max_train_samples 8
            --max_source_length {max_len}
            --max_target_length {max_len}
            --do_train
            --num_train_epochs {str(UpperCamelCase__       )}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(UpperCamelCase__       )}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        '''.split()

    __magic_name__								=			F'''
            --do_eval
            --per_device_eval_batch_size 4
            --max_eval_samples 8
            --val_max_target_length {max_len}
            --evaluation_strategy steps
            --eval_steps {str(UpperCamelCase__       )}
        '''.split()

    __magic_name__								=			"""
            --do_predict
        """.split()

    __magic_name__								=			[]
    if do_train:
      args += args_train

    if do_eval:
      args += args_eval

    if do_predict:
      args += args_predict

    if predict_with_generate:
      args += "--predict_with_generate".split()

    if do_train:
      if optim == "adafactor":
        args += "--adafactor".split()
      else:
        args += F'''--optim {optim}'''.split()

    if extra_args_str is not None:
      args += extra_args_str.split()

    if distributed:
      if n_gpus_to_use is None:
        __magic_name__								=			get_gpu_count()
      __magic_name__								=			get_torch_dist_unique_port()
      __magic_name__								=			F'''
                -m torch.distributed.run
                --nproc_per_node={n_gpus_to_use}
                --master_port={master_port}
                {self.examples_dir_str}/pytorch/translation/run_translation.py
            '''.split()
      __magic_name__								=			[sys.executable] + distributed_args + args
      # keep for quick debug
      # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
      execute_subprocess_async(UpperCamelCase__     ,				env=self.get_env()       )
    else:
      __magic_name__								=			["""run_translation.py"""] + args
      with patch.object(UpperCamelCase__     ,				"""argv"""     ,				UpperCamelCase__       ):
        main()

    return output_dir



import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD




class 			UpperCAmelCase_     (					nn.Module	):


  '''simple docstring'''







  a__       			=						42
  a__       			=						42
  a__       			=						0.0
  a__       			=						1
  a__       			=						1
  a__       			=						True
  a__       			=						False
  a__       			=						False
  a__       			=						False
  a__       			=						jnp.floataa
  def 				_lowercase				(					self     :					Optional[Any]       )     ->      Tuple:







    """simple docstring"""






    __magic_name__								=			[]
    __magic_name__								=			[]

    for i in range(self.num_layers       ):
      __magic_name__								=			self.in_channels if i == 0 else self.out_channels

      __magic_name__								=			FlaxResnetBlockaD(
          in_channels=UpperCamelCase__     ,				out_channels=self.out_channels     ,				dropout_prob=self.dropout     ,				dtype=self.dtype     ,				)
      resnets.append(UpperCamelCase__       )

      __magic_name__								=			FlaxTransformeraDModel(
          in_channels=self.out_channels     ,				n_heads=self.num_attention_heads     ,				d_head=self.out_channels // self.num_attention_heads     ,				depth=1     ,				use_linear_projection=self.use_linear_projection     ,				only_cross_attention=self.only_cross_attention     ,				use_memory_efficient_attention=self.use_memory_efficient_attention     ,				dtype=self.dtype     ,				)
      attentions.append(UpperCamelCase__       )

    __magic_name__								=			resnets
    __magic_name__								=			attentions

    if self.add_downsample:
      __magic_name__								=			FlaxDownsampleaD(self.out_channels     ,				dtype=self.dtype       )





  def __call__(					self     :					List[Any]     ,				UpperCamelCase__     :					Optional[int]     ,				UpperCamelCase__     :					Tuple     ,				UpperCamelCase__     :					Dict     ,				UpperCamelCase__     :					Optional[int]=True       )     ->      Any:







    """simple docstring"""






    __magic_name__								=			()

    for resnet, attn in zip(self.resnets     ,				self.attentions       ):
      __magic_name__								=			resnet(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )
      __magic_name__								=			attn(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )
      output_states += (hidden_states,)

    if self.add_downsample:
      __magic_name__								=			self.downsamplers_a(UpperCamelCase__       )
      output_states += (hidden_states,)

    return hidden_states, output_states







class 			UpperCAmelCase_     (					nn.Module	):


  '''simple docstring'''







  a__       			=						42
  a__       			=						42
  a__       			=						0.0
  a__       			=						1
  a__       			=						True
  a__       			=						jnp.floataa
  def 				_lowercase				(					self     :					str       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			[]

    for i in range(self.num_layers       ):
      __magic_name__								=			self.in_channels if i == 0 else self.out_channels

      __magic_name__								=			FlaxResnetBlockaD(
          in_channels=UpperCamelCase__     ,				out_channels=self.out_channels     ,				dropout_prob=self.dropout     ,				dtype=self.dtype     ,				)
      resnets.append(UpperCamelCase__       )
    __magic_name__								=			resnets

    if self.add_downsample:
      __magic_name__								=			FlaxDownsampleaD(self.out_channels     ,				dtype=self.dtype       )





  def __call__(					self     :					str     ,				UpperCamelCase__     :					Tuple     ,				UpperCamelCase__     :					Union[str, Any]     ,				UpperCamelCase__     :					int=True       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			()

    for resnet in self.resnets:
      __magic_name__								=			resnet(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )
      output_states += (hidden_states,)

    if self.add_downsample:
      __magic_name__								=			self.downsamplers_a(UpperCamelCase__       )
      output_states += (hidden_states,)

    return hidden_states, output_states







class 			UpperCAmelCase_     (					nn.Module	):


  '''simple docstring'''







  a__       			=						42
  a__       			=						42
  a__       			=						42
  a__       			=						0.0
  a__       			=						1
  a__       			=						1
  a__       			=						True
  a__       			=						False
  a__       			=						False
  a__       			=						False
  a__       			=						jnp.floataa
  def 				_lowercase				(					self     :					Optional[Any]       )     ->      Any:







    """simple docstring"""






    __magic_name__								=			[]
    __magic_name__								=			[]

    for i in range(self.num_layers       ):
      __magic_name__								=			self.in_channels if (i == self.num_layers - 1) else self.out_channels
      __magic_name__								=			self.prev_output_channel if i == 0 else self.out_channels

      __magic_name__								=			FlaxResnetBlockaD(
          in_channels=resnet_in_channels + res_skip_channels     ,				out_channels=self.out_channels     ,				dropout_prob=self.dropout     ,				dtype=self.dtype     ,				)
      resnets.append(UpperCamelCase__       )

      __magic_name__								=			FlaxTransformeraDModel(
          in_channels=self.out_channels     ,				n_heads=self.num_attention_heads     ,				d_head=self.out_channels // self.num_attention_heads     ,				depth=1     ,				use_linear_projection=self.use_linear_projection     ,				only_cross_attention=self.only_cross_attention     ,				use_memory_efficient_attention=self.use_memory_efficient_attention     ,				dtype=self.dtype     ,				)
      attentions.append(UpperCamelCase__       )

    __magic_name__								=			resnets
    __magic_name__								=			attentions

    if self.add_upsample:
      __magic_name__								=			FlaxUpsampleaD(self.out_channels     ,				dtype=self.dtype       )





  def __call__(					self     :					Optional[int]     ,				UpperCamelCase__     :					Any     ,				UpperCamelCase__     :					Dict     ,				UpperCamelCase__     :					Optional[int]     ,				UpperCamelCase__     :					Optional[Any]     ,				UpperCamelCase__     :					Dict=True       )     ->      Union[str, Any]:







    """simple docstring"""






    for resnet, attn in zip(self.resnets     ,				self.attentions       ):
      # pop res hidden states
      __magic_name__								=			res_hidden_states_tuple[-1]
      __magic_name__								=			res_hidden_states_tuple[:-1]
      __magic_name__								=			jnp.concatenate((hidden_states, res_hidden_states)     ,				axis=-1       )

      __magic_name__								=			resnet(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )
      __magic_name__								=			attn(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )

    if self.add_upsample:
      __magic_name__								=			self.upsamplers_a(UpperCamelCase__       )

    return hidden_states







class 			UpperCAmelCase_     (					nn.Module	):


  '''simple docstring'''







  a__       			=						42
  a__       			=						42
  a__       			=						42
  a__       			=						0.0
  a__       			=						1
  a__       			=						True
  a__       			=						jnp.floataa
  def 				_lowercase				(					self     :					List[Any]       )     ->      str:







    """simple docstring"""






    __magic_name__								=			[]

    for i in range(self.num_layers       ):
      __magic_name__								=			self.in_channels if (i == self.num_layers - 1) else self.out_channels
      __magic_name__								=			self.prev_output_channel if i == 0 else self.out_channels

      __magic_name__								=			FlaxResnetBlockaD(
          in_channels=resnet_in_channels + res_skip_channels     ,				out_channels=self.out_channels     ,				dropout_prob=self.dropout     ,				dtype=self.dtype     ,				)
      resnets.append(UpperCamelCase__       )

    __magic_name__								=			resnets

    if self.add_upsample:
      __magic_name__								=			FlaxUpsampleaD(self.out_channels     ,				dtype=self.dtype       )





  def __call__(					self     :					List[Any]     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					Union[str, Any]     ,				UpperCamelCase__     :					Optional[Any]     ,				UpperCamelCase__     :					str=True       )     ->      Dict:







    """simple docstring"""






    for resnet in self.resnets:
      # pop res hidden states
      __magic_name__								=			res_hidden_states_tuple[-1]
      __magic_name__								=			res_hidden_states_tuple[:-1]
      __magic_name__								=			jnp.concatenate((hidden_states, res_hidden_states)     ,				axis=-1       )

      __magic_name__								=			resnet(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )

    if self.add_upsample:
      __magic_name__								=			self.upsamplers_a(UpperCamelCase__       )

    return hidden_states







class 			UpperCAmelCase_     (					nn.Module	):


  '''simple docstring'''







  a__       			=						42
  a__       			=						0.0
  a__       			=						1
  a__       			=						1
  a__       			=						False
  a__       			=						False
  a__       			=						jnp.floataa
  def 				_lowercase				(					self     :					List[Any]       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			[
        FlaxResnetBlockaD(
            in_channels=self.in_channels     ,				out_channels=self.in_channels     ,				dropout_prob=self.dropout     ,				dtype=self.dtype     ,				)
    ]

    __magic_name__								=			[]

    for _ in range(self.num_layers       ):
      __magic_name__								=			FlaxTransformeraDModel(
          in_channels=self.in_channels     ,				n_heads=self.num_attention_heads     ,				d_head=self.in_channels // self.num_attention_heads     ,				depth=1     ,				use_linear_projection=self.use_linear_projection     ,				use_memory_efficient_attention=self.use_memory_efficient_attention     ,				dtype=self.dtype     ,				)
      attentions.append(UpperCamelCase__       )

      __magic_name__								=			FlaxResnetBlockaD(
          in_channels=self.in_channels     ,				out_channels=self.in_channels     ,				dropout_prob=self.dropout     ,				dtype=self.dtype     ,				)
      resnets.append(UpperCamelCase__       )

    __magic_name__								=			resnets
    __magic_name__								=			attentions





  def __call__(					self     :					Optional[int]     ,				UpperCamelCase__     :					Any     ,				UpperCamelCase__     :					Any     ,				UpperCamelCase__     :					List[Any]     ,				UpperCamelCase__     :					int=True       )     ->      str:







    """simple docstring"""






    __magic_name__								=			self.resnets[0](UpperCamelCase__     ,				UpperCamelCase__       )
    for attn, resnet in zip(self.attentions     ,				self.resnets[1:]       ):
      __magic_name__								=			attn(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )
      __magic_name__								=			resnet(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )

    return hidden_states

